PROJECT SUMMARY The natural experiment of human genetic variation can be used to infer structure-function relationships for key disease genes. We have previously demonstrated that population-scale genetic variation data can be harnessed to illuminate structure-function relationships for genes causative of the Mendelian disease hypertrophic cardiomyopathy. However, due to the rarity of individual causative variants, population genetics is ultimately limiting to the goal of understanding the functional importance of the entire coding region of any specific gene. There is an urgent need for experimental alternatives. Here, we propose to introduce targeted genetic variation into human induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) at scale (Aim 1). We propose two complementary strategies for deep mutational scanning of the most common genes causing hypertrophic cardiomyopathy, MYH7, MYBPC3 and TNNT2. The first, CRISPR-X, is a fusion of a cytidine deaminase (AID) with nuclease-inactive Cas9 (dCas9), and provides targeted mutational coverage in situ. The second, POPcode, uses a uracilated gene template and a set of mutant oligos to create an allelic library, which is then integrated into the genome using a Dual-Integrase Cassette Exchange (DICE). To characterize these cells, we further develop a custom microfluidics-based, fast optical method to phenotype single cells in real time (Aim 2). Predictions of pathogenicity according to both cell size and a fluorescence marker of the hypertrophy expression program will be mapped to 3D protein structures using our spatial scanning approach and tested against gold standard adjudicated patient variant data. Finally, we will investigate variant-specific mechanisms of disease using single cell RNA sequencing to assess the effect of each variant on allelic stoichiometry and transcriptional programming, as well as protein biochemistry to assess sarcomere protein interaction and power generation (Aim 3). In summary, we plan comprehensive evaluation of all potential coding variation in the most frequently causative genes for the most common Mendelian cardiovascular disease. Using innovative phenotyping tools and novel statistical approaches to the integration of population and cellular data, we aim to understand the structure and function of these genes in health and disease, providing an experimental basis for the classification of genetic variants in the clinical setting.